Beyond Remote Device Management
A Strategic Guide to Edge Computing and Application Lifecycle Orchestration
for Enterprise Business Leaders
Executive Summary
The digital transformation landscape is rapidly evolving, and organizations that rely solely on traditional remote device management are finding themselves at a competitive disadvantage. As we move into an era where by 2029, Gartner predicts that 50% of enterprises will be using edge computing, up from 20% in 20241, the need for sophisticated edge computing solutions has never been more critical.
This guide explores the fundamental differences between basic remote device management and a comprehensive edge computing platform with application lifecycle orchestration. While traditional remote device management focuses primarily on hardware monitoring and basic updates, an edge computing platform with application lifecycle management provides the deep visibility, security, and control necessary to leverage local processing and advanced data management capabilities that modern enterprises require.
Gartner has identified eight submarkets within edge computing2 – edge management and orchestration, Internet of Things (IoT), edge data management, edge analytics and machine learning (including generative AI), edge computing server solutions, edge communications infrastructure, data center and content delivery network (CDN) edge services, and edge vertical industry solutions – indicating the complexity and opportunity within this space. Organizations that implement strategic edge computing initiatives are positioned to achieve significant competitive advantages through reduced latency, enhanced security, improved operational efficiency, and new revenue streams.
1 Gartner, Strategic Roadmap for Edge Computing, April 2025
2 Gartner, Market Guide for Edge Computing, June 2025
The Evolution from Remote Device Management to Edge Computing
The Limitations of Traditional Remote Device Management
Traditional remote device management solutions emerged from the need to monitor and maintain distributed hardware assets. These systems typically focus on:
- Device Status Monitoring: Basic health checks and connectivity verification
- Firmware Updates: Scheduled or manual updates to device software
- Configuration Management: Remote adjustment of device settings
- Asset Tracking: Inventory and location management of deployed devices
While these capabilities are important, they represent only the foundational layer of what’s needed for modern distributed computing environments. Traditional remote device management solutions are inherently hardware-centric and lack the sophisticated application orchestration capabilities required for today’s edge computing use cases.
The Edge Computing Paradigm
Edge computing represents a fundamental shift in how we think about distributed systems. Edge computing offerings provide compute services where network latency, bandwidth, data sovereignty or disconnected operational requirements cannot be effectively addressed solely via remote, central data centers.
According to Gartner’s Market Guide for Edge Computing, edge computing solutions address four critical requirements:
- Real-time Computing Services: When depending on remote data centers would introduce unacceptable latency
- Network Traffic and Cost Reduction: Managing high-volume local data more efficiently
- Semi-autonomy: Operating effectively during disconnection from central systems
- Data Sovereignty and Control: Keeping regulated or confidential data within specific
Remote Device Management Compared to Edge Computing Orchestration
The Business Impact of This Evolution
Edge computing solutions are often part of specific business improvements (e.g., improving the customer experience), or operations improvements (e.g., factory automation). The shift from basic remote device management to comprehensive edge computing orchestration enables organizations to:
- Transform Business Operations: Move from reactive maintenance to predictive optimization
- Enable New Revenue Streams: Support for AI-driven services and real-time analytics
- Reduce Operational Costs: Automated management reduces the need for on-site technical staff
- Improve Compliance: Enhanced data governance and regulatory compliance capabilities
- Accelerate Innovation: Faster deployment and iteration of edge applications
Understanding Edge Computing with Application Lifecycle Management
The diagram below illustrates a modern four-layer architecture that distinguishes modern edge computing platforms from traditional remote device management solutions. At the foundation, the Infrastructure Layer provides bare-metal hardware abstraction with virtualization and containerization support, enabling optimal resource allocation while integrating hardware security modules for zero-trust protection. The Platform Layer builds upon this foundation with edge operating systems like EVE-OS, container orchestration through Kubernetes at the edge, microservices architecture, and API-driven management interfaces that ensure consistent operations across diverse hardware environments. The Application Layer enables sophisticated application lifecycle management through dynamic deployment capabilities, version control with rollback mechanisms, A/B testing, and comprehensive performance monitoring. At the top, the Data Layer manages local data processing and analytics, real-time streaming pipelines, edge-to-cloud synchronization, and maintains data sovereignty and compliance controls.
An Application Lifecycle Management framework spans across all layers to orchestrate the complete application journey from development through retirement, encompassing edge-native development frameworks, automated deployment across heterogeneous hardware, real-time monitoring with predictive maintenance, and zero-downtime updates with compliance tracking.
This integrated architecture enables enterprises to move beyond simple device monitoring to full-scale edge orchestration with the security, scalability, and operational consistency required for modern distributed computing environments.
Edge Management and Orchestration (EMO): A Critical Component
By 2027, 20% of large enterprises will deploy an Edge Management and Orchestration (EMO) solution, compared with fewer than 1% in 2023, highlighting the growing recognition of EMO’s importance.
“Edge computing requires platforms that enable edge-native workloads, provide zero-touch management and integrate between the cloud and the edge. I&O leaders should choose edge computing platforms that are extensible for new and evolving workloads — including edge AI.” – GARTNER
The AI and Machine Learning Imperative
By 2029, at least 60% of edge computing deployments will use composite AI (both predictive and generative AI), compared to less than 5% in 2023 .
This dramatic increase underscores the importance of edge platforms that can support sophisticated AI workloads. Modern edge computing platforms must provide:
The Business Case: Financial Benefits and Return on Investment
Financial Benefits
Direct Cost Savings
- Reduced Bandwidth Costs: Local processing significantly reduces data transmission requirements
- Lower Cloud Computing Expenses: Edge processing reduces reliance on expensive cloud compute resources
- Decreased Downtime: Predictive maintenance and local processing reduce system outages
- Operational Efficiency: Automated management reduces on-site technical support and expensive truck rolls
Revenue Generation Opportunities
- New Service Offerings: Real-time analytics and AI-driven services create new revenue streams
- Enhanced Customer Experience: Reduced latency and improved responsiveness increase customer satisfaction and retention
- Improved Decision Making: Real-time insights enable faster, more informed business decisions •
- Competitive Advantage: First-mover advantage in edge-enabled services
Total Cost of Ownership (TCO) Considerations
Initial Investment Components
- Infrastructure Costs: Edge hardware, networking, and security infrastructure
- Software Licensing: Edge orchestration platforms and application licenses
- Integration Expenses: Connecting edge systems with existing enterprise infrastructure
- Training and Development: Upskilling teams for edge computing management
Operational Expenses
- Maintenance and Support: Ongoing system maintenance and technical support
- Connectivity Costs: Network connectivity for edge locations
- Security Management: Continuous security monitoring and threat response
- Compliance and Governance: Regulatory compliance and audit capabilities
Hidden Costs to Consider
- Skills Gap: Finding and retaining qualified edge computing professionals
- Vendor Lock-in: Potential future migration costs from proprietary solutions
- Scalability Limitations: Costs associated with platform limitations as requirements grow
- Integration Complexity: Unexpected costs from complex legacy system integration
Calculating ROI for Edge Computing Initiatives
A comprehensive ROI calculation for edge computing should include these quantifiable benefits:
- Operational Cost Savings: Reduced labor, bandwidth, and infrastructure costs
- Revenue Enhancement: New services, improved customer experience, increased throughput
- Risk Mitigation: Reduced downtime, improved security, regulatory compliance
- Efficiency Gains: Faster decision-making, automated processes, resource optimization
ROI Formula: ROI = (Total Benefits – Total Costs) / Total Costs × 100
Example Calculation for Manufacturing
- Initial Investment: $2.5M (hardware, software, integration)
- Annual Operational Costs: $500K
- Annual Benefits: $1.8M (reduced downtime, predictive maintenance, quality improvements)
- 3-Year ROI: ((1.8M × 3) – (2.5M + 0.5M × 3)) / (2.5M + 0.5M × 3) × 100 = 35%
4 Grandview Research, Edge Computing Market Summary – Link
CIO Survey Provides Key Insights
ZEDEDA partnered with Censuswide between February 26 and March 4, 2025 to conduct a survey of 301 US-based CIOs, specifically exploring enterprise investments in edge AI. Key findings and link to complete survey results below:
Customer Experience and Predictive Maintenance are Largest Initial Investment Areas
The primary focus of initial edge AI investments is on enhancing customer experience, risk management, cost reduction, and process acceleration. As expected, retail has been keenly focused on customer experience: 93% of retail CIOs state that they have deployed some type of edge AI for this purpose, compared to 80% across all sectors. However, future investment priorities are shifting towards cost reduction and risk management, process acceleration, and customer experience. Looking ahead, cost reduction (74%) and risk management (73%) are the leading priorities for future edge AI deployments in the next 12-24 months. Notably, manufacturing is prioritizing process acceleration. 82% of manufacturing companies in this sector are planning to deploy edge AI for this purpose in the next 12-24 months, compared to 68% across all sectors.
Security and Privacy are Both Key Drivers and Top Challenges for Edge AI
Security and privacy play a dual role in edge AI. CIOs cite improving security and data privacy (53%) as the top reason for investing in edge AI over cloud-based AI. Yet, security risks and data protection concerns (42%) are also identified as the leading challenges in edge AI deployments.
Multimodal AI Emerges as the Preferred Edge AI Model
Multimodal AI, which combines speech, text, and vision, is the most popular AI model currently running or planned for deployment at the edge. 60% of CIOs surveyed are running/planning to run multi- modal AI at the edge, similar to those running it in the cloud (59%). After multimodal Al models, speech recognition models (52%) were the next most popular. Notably, large language models (LLMs) ranked as popular as computer vision models for the edge, at 47% of currently or planned deployments. Explore more results of the survey.
“Edge computing is poised to redefine how businesses leverage real-time data, and its future hinges on tailored, industry-specific solutions that address unique operational demands. We’re seeing service providers double down on investments—building out low-latency networks, enhancing AI-driven edge analytics, and forging partnerships to deliver scalable, secure infrastructure. These efforts are critical to realizing the full potential of edge computing, enabling everything from smarter manufacturing floors to responsive healthcare systems, and ultimately driving a new wave of innovation across verticals.”
— DAVE MCCARTHY, RESEARCH VICE PRESIDENT CLOUD AND EDGE SERVICES AT IDC
IDC WORLDWIDE EDGE COMPUTING SPENDING GUIDE
Overcoming Enterprise Challenges at the Edge
Edge computing deployment presents four critical challenge categories that enterprises must address for successful implementation. Security challenges are fundamentally different from traditional IT environments, operational consistency becomes critical, but complex, skills and resource limitations create significant adoption barriers, and finally, regulatory and compliance complexity varies dramatically across industries.
SECURITY CHALLENGES
Edge computing introduces unique security challenges that traditional cybersecurity approaches cannot adequately address.
Physical Security Concerns
- Uncontrolled Environments
Edge devices often operate in locations without physical security controls - Tamper Resistance
Need for hardware-based security measures to prevent physical tampering - Theft and Vandalism
Protection against device theft and malicious physical access
Network Security Challenges
- Intermittent Connectivity
Security must function during network outages - Diverse Connection
Types Support for various connectivity options (cellular, satellite, microwave) - Network Segmentation
Isolation of edge networks from corporate infrastructure
Data Security Requirements
- Data Sovereignty
Ensuring data remains within specified geographic or regulatory boundaries - Encryption
End-to-end encryption for data in transit and at rest - Access Control
Fine-grained access control for distributed systems
OPERATIONAL CONSISTENCY
Managing edge infrastructure across diverse computing environments presents significant operational challenges.
Hardware Heterogeneity
- Diverse Form Factors
From embedded IoT devices to ruggedized industrial computers - Varying Capabilities
Different processing power, memory, and storage across devices - Hardware Lifecycle Management
Managing updates and replacements across different hardware generations
Software Standardization
- Operating System Diversity
Supporting multiple operating systems and versions - Application Compatibility
Ensuring applications work across different hardware platforms - Update Management
Coordinating software updates across heterogeneous environments
Network Connectivity Variations
- Bandwidth Limitations
Adapting to varying network speeds and reliability - Latency Considerations
Optimizing for different network conditions - Offline Operations
Maintaining functionality during network outages
SKILLS & RESOURCE LIMITATIONS
The edge computing technical skills gap represents a significant challenge for enterprise adoption.
Technical Skills Requirements
- Edge Architecture Design
Understanding distributed system architecture principles - DevOps and Orchestration
Container management and CI/CD pipeline expertise - Security Expertise
Specialized knowledge of edge security requirements - AI/ML Operations
Skills in deploying and managing AI models at the edge
Organizational Challenges
- Cross-functional Collaboration
Coordination between IT, OT, and business teams - Change Management
Adapting organizational processes for edge computing - Vendor Management
Coordinating multiple technology vendors and partners
COMPLIANCE COMPLEXITY
Regulatory compliance presents another critical barrier, particularly in industries that are heavily regulated.
Compliance Challenges
- Data Governance
Ensuring data handling compliance across distributed locations - Audit Trails
Maintaining comprehensive audit logs across edge deployments - Regulatory Reporting
Automated compliance reporting from edge locations - Cross-border Compliance
Managing different regulatory requirements across geographic regions
Industry-Specific Requirements
- Healthcare
HIPAA compliance for patient data processing - Financial Services
PCI DSS compliance for payment processing - Manufacturing
ISO 27001 and industry safety standards - Energy
NERC CIP compliance for critical infrastructure protection
“ZEDEDA provides a cloud-based solution, orchestrating the software lifecycle of our IoT devices deployed worldwide. This enables frequent and secure updates of new advanced software, which are required for our customers who operate, more than ever, in an agile business.”
SERGE MORISOD, HEAD OF IOT LAB, BOBST
The Power of a Consistent Edge Platform
Overcoming Edge Challenges with Purpose-Built Solutions
To address the challenges outlined in the previous section, enterprises need an consistent edge computing platform that can provide standardized capabilities across diverse environments. Two key technologies have emerged as foundational elements of such platforms.
Edge Virtualization Engine (EVE)
The Linux Foundation Edge community project, EVE (Edge Virtualization Engine) is an open source edge computing operating system designed specifically for distributed edge infrastructure:
- Hardware Abstraction: EVE provides a consistent interface across diverse hardware, from IoT gateways to edge servers
- Zero Trust Security: Built with a security-first architecture that assumes hostile environments
- Lightweight Virtualization: Uses lightweight containers and VMs optimized for resource-constrained devices
- Autonomous Operation: Designed to function during network outages with local decision-making capability
- Orchestration API: Offers comprehensive remote management interface
“The intelligent edge requires solutions that can meet the real-time requirements of safety-critical environments. Our work with ZEDEDA combines Wind River’s expertise in mission-critical edge computing with ZEDEDA’s streamlined orchestration capabilities. This collaboration is particularly valuable for industries deploying AI in environments where reliability and security are non-negotiable.”
– Avijit Sinha, Sr. Vice President, Strategy and Global Business Development at Wind River
ZEDEDA Edge Computing Platform
Building on the EVE foundation, ZEDEDA has developed an enterprise-grade edge computing platform specifically designed for deploying and managing AI workloads at the edge:
- Zero Touch Provisioning: Enables rapid deployment without on-site technical personnel
- Application Orchestration: Simplifies management of AI applications across distributed locations
- Hardware-Agnostic: Supports diverse compute platforms from major OEMs
- Secure Supply Chain: Provides verified measured boot and trusted execution environment
- AI-Optimized: Consistent platform capabilities including role-based access control, security, infrastructure orchestration, application update, rollback and more for AI model deployment and management
Real-World Implementation Success
- Deployment Efficiency: 75% reduction in deployment time
- Scalability: Support for large-scale distributed deployments
- Future Growth: Foundation for additional value-added services
- Operational Simplification: Streamlined provisioning and management processes
“With ZEDEDA we’ve been able to reduce the overall device provisioning and field installation time by up to 75%.”
– IVAN ARKIPOFF, CTO, PV HARDWARE
5 ZEDEDA Customer Case Study – Link
Key Takeaways and Additional Resources
Strategic Imperatives for Enterprise Success
- Think Strategically, Start Tactically: As Gartner recommends : “It’s important to start small but think big with edge computing. From the very beginning, it’s important to plan strategically, consider future extensibility, start choosing frameworks and standards, ask vendors what a future roadmap looks like and consider how vendors can help you on your strategy.”
- Prioritize Open Standards and Platforms: Choose ecosystems of partners who can help you on your overall edge computing journey and accelerate your innovation. Capabilities within a vertical industry are much more important than capabilities for a single use case. A strong, flexible ecosystem is more important than a single, strong vendor.
- Focus on Extensibility from Day One: Enterprises are usually initially focused on use cases versus platforms and architectures but inevitably require platforms and architectures for extensibility. Planning for extensibility from the beginning prevents costly re-architecture later.
- Embrace Zero-Trust Security: The distributed nature of edge computing requires a fundamentally different approach to security. Zero-trust architecture with hardware-rooted security (TPM-based attestation) should be the foundation of any edge computing deployment.
- Plan for the AI-Driven Future By 2029: Gartner predicts at least 60% of edge computing deployments will use composite AI (both predictive and generative AI).6 Organizations that plan for AI integration from the beginning will have significant competitive advantages.
Additional Resources
For technology professionals tasked with building edge computing architectures and preparing business cases, the following resources provide valuable additional information:
Technical Resources
- Linux Foundation Edge Documentation – Comprehensive technical information on the EVE platform
- ZEDEDA Developer Help Center – Technical guidance for implementing edge computing with ZEDEDA
- ZEDEDA Edge Academy – Featured ZEDEDA Training Course Catalog
- ZEDEDA Web Site – Remote Device Management Overview
Business & Strategy Resources
- Monetizing AI at the Enterprise Edge
- A Buyers Guide to Edge Computing Platforms
- IDC Worldwide Edge Computing Spending Guide Media Release
- 2025 ZEDEDA Edge AI Survey of 301 US-based CIOs
6 Gartner, Market Guide for Edge Computing, March 2024
ZEDEDA makes edge computing effortless, open and intrinsically secure — extending the cloud experience to the edge. ZEDEDA reduces the cost of managing and orchestrating distributed edge infrastructure and applications while increasing visibility, security and control. ZEDEDA delivers instant time to value, has tens of thousands of nodes under management and is backed by world-class investors with teams in the United States, Germany, India, and Abu Dhabi, UAE. For more information, visit www.ZEDEDA.com
